Source code for

# -*- coding: utf-8 -*-
"""Cluster lineages module."""

from types import MappingProxyType
from typing import Dict, Tuple, Union, TypeVar, Optional, Sequence
from pathlib import Path
from collections import Iterable

import numpy as np
from sklearn.preprocessing import StandardScaler

import matplotlib.pyplot as plt

from cellrank import logging as logg
from cellrank.ul._docs import d
from import (
from import save_fig, _unique_order_preserving
from cellrank.ul._utils import _get_n_cores, _check_collection
from import _DEFAULT_BACKEND, AbsProbKey

AnnData = TypeVar("AnnData")
Queue = TypeVar("Queue")

[docs]@d.dedent def cluster_lineage( adata: AnnData, model: _input_model_type, genes: Sequence[str], lineage: str, backward: bool = False, time_range: _time_range_type = None, clusters: Optional[Sequence[str]] = None, n_points: int = 200, time_key: str = "latent_time", norm: bool = True, recompute: bool = False, callback: _callback_type = None, ncols: int = 3, sharey: Union[str, bool] = False, key: Optional[str] = None, random_state: Optional[int] = None, use_leiden: bool = False, show_progress_bar: bool = True, n_jobs: Optional[int] = 1, backend: str = _DEFAULT_BACKEND, figsize: Optional[Tuple[float, float]] = None, dpi: Optional[int] = None, save: Optional[Union[str, Path]] = None, pca_kwargs: Dict = MappingProxyType({"svd_solver": "arpack"}), neighbors_kwargs: Dict = MappingProxyType({"use_rep": "X"}), clustering_kwargs: Dict = MappingProxyType({}), return_models: bool = False, **kwargs, ) -> Optional[_return_model_type]: """ Cluster gene expression trends within a lineage and plot the clusters. This function is based on Palantir, see [Setty19]_. It can be used to discover modules of genes that drive development along a given lineage. Consider running this function on a subset of genes which are potential lineage drivers, identified e.g. by running :func:``. Parameters ---------- %(adata)s %(model)s %(genes)s lineage Name of the lineage for which to cluster the genes. %(backward)s %(time_ranges)s clusters Cluster identifiers to plot. If `None`, all clusters will be considered. Useful when plotting previously computed clusters. n_points Number of points used for prediction. time_key Key in ``adata.obs`` where the pseudotime is stored. norm Whether to z-normalize each trend to have zero mean, unit variance. recompute If `True`, recompute the clustering, otherwise try to find already existing one. %(model_callback)s ncols Number of columns for the plot. sharey Whether to share y-axis across multiple plots. key Key in ``adata.uns`` where to save the results. If `None`, it will be saved as ``lineage_{lineage}_trend`` . random_state Random seed for reproducibility. use_leiden Whether to use :func:`` for clustering or :func:``. %(parallel)s %(plotting)s pca_kwargs Keyword arguments for :func:`scanpy.pp.pca`. neighbors_kwargs Keyword arguments for :func:`scanpy.pp.neighbors`. clustering_kwargs Keyword arguments for :func:`` or :func:``. %(return_models)s **kwargs: Keyword arguments for :meth:`cellrank.ul.models.BaseModel.prepare`. Returns ------- %(plots_or_returns_models)s Also updates ``adata.uns`` with the following: - ``key`` or ``lineage_{lineage}_trend`` - an :class:`anndata.AnnData` object of shape `(n_genes, n_points)` containing the clustered genes. """ import scanpy as sc from anndata import AnnData as _AnnData lineage_key = str(AbsProbKey.BACKWARD if backward else AbsProbKey.FORWARD) if lineage_key not in adata.obsm: raise KeyError(f"Lineages key `{lineage_key!r}` not found in `adata.obsm`.") _ = adata.obsm[lineage_key][lineage] genes = _unique_order_preserving(genes) _check_collection(adata, genes, "var_names", kwargs.get("use_raw", False)) if key is None: key = f"lineage_{lineage}_trend" if recompute or key not in adata.uns: kwargs["backward"] = backward kwargs["time_key"] = time_key kwargs["n_test_points"] = n_points models = _create_models(model, genes, [lineage]) all_models, models, genes, _ = _fit_bulk( models, _create_callbacks(adata, callback, genes, [lineage], **kwargs), genes, lineage, time_range, return_models=True, # always return (better error messages) filter_all_failed=True, parallel_kwargs={ "show_progress_bar": show_progress_bar, "n_jobs": _get_n_cores(n_jobs, len(genes)), "backend": _get_backend(models, backend), }, **kwargs, ) # `n_genes, n_test_points` trends = np.vstack([model[lineage].y_test for model in models.values()]).T if norm: logg.debug("Normalizing trends") _ = StandardScaler(copy=False).fit_transform(trends) trends = _AnnData(trends.T) trends.obs_names = genes # sanity check if trends.n_obs != len(genes): raise RuntimeError( f"Expected to find `{len(genes)}` genes, found `{trends.n_obs}`." ) if trends.n_vars != n_points: raise RuntimeError( f"Expected to find `{n_points}` points, found `{trends.n_vars}`." ) random_state = np.random.mtrand.RandomState(random_state).randint(2 ** 16) pca_kwargs = dict(pca_kwargs) pca_kwargs.setdefault("n_comps", min(50, n_points, len(genes)) - 1) pca_kwargs.setdefault("random_state", random_state) sc.pp.pca(trends, **pca_kwargs) neighbors_kwargs = dict(neighbors_kwargs) neighbors_kwargs.setdefault("random_state", random_state) sc.pp.neighbors(trends, **neighbors_kwargs) clustering_kwargs = dict(clustering_kwargs) clustering_kwargs["key_added"] = "clusters" clustering_kwargs.setdefault("random_state", random_state) try: if use_leiden:, **clustering_kwargs) else:, **clustering_kwargs) except ImportError as e: logg.warning(str(e)) if use_leiden:, **clustering_kwargs) else:, **clustering_kwargs)"Saving data to `adata.uns[{key!r}]`") adata.uns[key] = trends else: all_models = None"Loading data from `adata.uns[{key!r}]`") trends = adata.uns[key] if "clusters" not in trends.obs: raise KeyError("Unable to find the clustering in `trends.obs['clusters']`.") if clusters is None: clusters = trends.obs["clusters"].cat.categories for c in clusters: if c not in trends.obs["clusters"].cat.categories: raise ValueError( f"Invalid cluster name `{c!r}`. " f"Valid options are `{list(trends.obs['clusters'].cat.categories)}`." ) nrows = int(np.ceil(len(clusters) / ncols)) fig, axes = plt.subplots( nrows, ncols, figsize=(ncols * 10, nrows * 10) if figsize is None else figsize, sharey=sharey, dpi=dpi, ) if not isinstance(axes, Iterable): axes = [axes] axes = np.ravel(axes) j = 0 for j, (ax, c) in enumerate(zip(axes, clusters)): # noqa data = trends[trends.obs["clusters"] == c].X mean, sd = np.mean(data, axis=0), np.var(data, axis=0) sd = np.sqrt(sd) for i in range(data.shape[0]): ax.plot(data[i], color="gray", lw=0.5) ax.plot(mean, lw=2, color="black") ax.plot(mean - sd, lw=1.5, color="black", linestyle="--") ax.plot(mean + sd, lw=1.5, color="black", linestyle="--") ax.fill_between( range(len(mean)), mean - sd, mean + sd, color="black", alpha=0.1 ) ax.set_title(f"Cluster {c}") ax.set_xticks([]) if not sharey: ax.set_yticks([]) for j in range(j + 1, len(axes)): axes[j].remove() if save is not None: save_fig(fig, save) if return_models: return all_models